Medical Math: Mathematicians doing cancer research

In a few years, a mathematician may be part of every medical team that treats a cancer patient.

Today, a typical workday finds Anderson, Gatenby and other researchers brainstorming in a room they call the "collaboratorium." There are no test tubes or microscopes; the space is filled with comfortable couches, the smell of coffee and a giant chalkboard on which the researchers have scrawled a blizzard of numbers, lines and diagrams that they constantly tweak and reformulate.

The team's goal, in a way, is to mimic in cancer research what hurricane forecasters have accomplished. Meteorologists plug a host of variables — wind speed, water temperature, air temperature, barometric pressures, for example — into formulas that predict how a hurricane will behave. For the oncological math team, the variables may be cell type, the presence of a certain gene, a tumor's rate of growth or some other attribute of a cancer or characteristic of the organ where the cancer is growing. Their quest is to determine which variables, in the right formula, will predict how a given cancer will behave — and ultimately, how to treat it.

One promising area of study for the team involves gliomas, a common form of brain cancer that is uniformly fatal. All gliomas are not created equal, however. Some patients live with low-grade gliomas for decades, while other gliomas become high-grade glioblastomas and kill the patient within in a matter of months.

Traditional methods for predicting how fast a glioma will progress — using data from scans and microscopic examination of blood vessel growth — haven't been very accurate. In collaboration with researchers at the University of Washington in Seattle, Anderson and his colleagues at Moffitt developed mathematical models that incorporated more variables, including cell appearance, for example.

Ultimately, they developed formulas that, in simulations, matched the growth patterns of actual patients' tumors. Anderson says the model may provide a "powerful clinical tool" that will help physicians better predict the likely course of an individual's disease.

The group is also using mathematical modeling to better understand and predict how fast prostate cancer, melanoma, sarcomas, leukemia and other malignancies will spread. In 2011, Anderson and a group of his colleagues landed a $3-million grant from the National Institutes of Health to create mathematical models to predict how aggressively prostate cancer will proceed. "I think it's a really exciting time for the field. I think we're on the cusp of an explosion of this type of research," says Anderson.

In time, Anderson expects to be able to create models that can serve as treatment-planning tools for every cancer. "We want to be taking a patient, get a biopsy from them, get their imaging, get their blood work, get as much information from them as we can" and plug the information into a model that will predict how the tumor will respond to various treatments.

Within a decade, say Anderson and Gatenby, a mathematician will be part of every cancer patient's medical team, participating in clinical decision-making. "The bottom line is, how can we make clinical care better," says Gatenby. "And that's the goal."